Characteristics and risk factors for post-COVID-19 breathlessness after hospitalisation for COVID-19.

Background: Persistence of respiratory symptoms, particularly breathlessness, after acute coronavirus disease 2019 (COVID-19) infection has emerged as a significant clinical problem. We aimed to characterise and identify risk factors for patients with persistent breathlessness following COVID-19 hospitalisation. Methods: PHOSP-COVID is a multicentre prospective cohort study of UK adults hospitalised for COVID-19. Clinical data were collected during hospitalisation and at a follow-up visit. Breathlessness was measured by a numeric rating scale of 0-10. We defined post-COVID-19 breathlessness as an increase in score of ≥1 compared to the pre-COVID-19 level. Multivariable logistic regression was used to identify risk factors and to develop a prediction model for post-COVID-19 breathlessness. Results: We included 1226 participants (37% female, median age 59 years, 22% mechanically ventilated). At a median 5 months after discharge, 50% reported post-COVID-19 breathlessness. Risk factors for post-COVID-19 breathlessness were socioeconomic deprivation (adjusted OR 1.67, 95% CI 1.14-2.44), pre-existing depression/anxiety (adjusted OR 1.58, 95% CI 1.06-2.35), female sex (adjusted OR 1.56, 95% CI 1.21-2.00) and admission duration (adjusted OR 1.01, 95% CI 1.00-1.02). Black ethnicity (adjusted OR 0.56, 95% CI 0.35-0.89) and older age groups (adjusted OR 0.31, 95% CI 0.14-0.66) were less likely to report post-COVID-19 breathlessness. Post-COVID-19 breathlessness was associated with worse performance on the shuttle walk test and forced vital capacity, but not with obstructive airflow limitation. The prediction model had fair discrimination (concordance statistic 0.66, 95% CI 0.63-0.69) and good calibration (calibration slope 1.00, 95% CI 0.80-1.21). Conclusions: Post-COVID-19 breathlessness was commonly reported in this national cohort of patients hospitalised for COVID-19 and is likely to be a multifactorial problem with physical and emotional components.

IoT-based disease prediction using machine learning.

COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform.

Characteristics and risk factors for post-COVID breathlessness after hospitalisation for COVID-19

Background Persistence of respiratory symptoms—particularly breathlessness—after acute COVID-19 infection has emerged as a significant clinical problem. We aimed to characterise and identify risk factors for patients with persistent breathlessness following COVID-19 hospitalisation. Methods PHOSP-COVID is a multi-centre prospective cohort study of UK adults hospitalised for COVID-19. Clinical data were collected during hospitalisation and at a follow-up visit. Breathlessness was measured by a numeric rating scale of 0–10. We defined post-COVID breathlessness as an increase in score of 1 or more compared to the pre-COVID-19 level. Multivariable logistic regression was used to identify risk factors, and to develop a prediction model for post-COVID breathlessness. Results We included 1226 participants (37% female, median age 59 years, 22% mechanically ventilated). At a median five months after discharge, 50% reported post-COVID breathlessness. Risk factors for post-COVID breathlessness were socio-economic deprivation (adjusted odds ratio, 1.67;95% confidence interval, 1.14–2.44), pre-existing depression/anxiety (1.58;1.06–2.35), female sex (1.56;1.21–2.00) and admission duration (1.01;1.00–1.02). Black ethnicity (0.56;0.35–0.89) and older age groups (0.31;0.14–0.66) were less likely to report post-COVID breathlessness. Post-COVID breathlessness was associated with worse performance on the shuttle walk test and forced vital capacity, but not with obstructive airflow limitation. The prediction model had fair discrimination (concordance-statistic 0.66;0.63–0.69), and good calibration (calibration slope 1.00;0.80–1.21). Conclusions Post-COVID breathlessness was commonly reported in this national cohort of patients hospitalised for COVID-19 and is likely to be a multifactorial problem with physical and emotional components.

Prevalence, risk factors and treatments for post-COVID-19 breathlessness: a systematic review and meta-analysis.

Persistent breathlessness >28 days after acute COVID-19 infection has been identified as a highly debilitating post-COVID symptom. However, the prevalence, risk factors, mechanisms and treatments for post-COVID breathlessness remain poorly understood. We systematically searched PubMed and Embase for relevant studies published from 1 January 2020 to 1 November 2021 (PROSPERO registration number: CRD42021285733) and included 119 eligible papers. Random-effects meta-analysis of 42 872 patients with COVID-19 reported in 102 papers found an overall prevalence of post-COVID breathlessness of 26% (95% CI 23-29) when measuring the presence/absence of the symptom, and 41% (95% CI 34-48) when using Medical Research Council (MRC)/modified MRC dyspnoea scale. The pooled prevalence decreased significantly from 1-6 months to 7-12 months post-infection. Post-COVID breathlessness was more common in those with severe/critical acute infection, those who were hospitalised and females, and was less likely to be reported by patients in Asia than those in Europe or North America. Multiple pathophysiological mechanisms have been proposed (including deconditioning, restrictive/obstructive airflow limitation, systemic inflammation, impaired mental health), but the body of evidence remains inconclusive. Seven cohort studies and one randomised controlled trial suggested rehabilitation exercises may reduce post-COVID breathlessness. There is an urgent need for mechanistic research and development of interventions for the prevention and treatment of post-COVID breathlessness.

Chest X-ray and CT Scan Classification using Ensemble Learning through Transfer Learning

COVID-19 has posed an extraordinary challenge to the entire world. As the number of COVID-19 cases continues to climb around the world, medical experts are facing an unprecedented challenge in correctly diagnosing and predicting the disease. The present research attempts to develop a new and effective strategy for classifying chest X-rays and CT Scans in order to distinguish COVID-19 from other diseases. Transfer learning was used to train various models for chest X-rays and CT Scan, including Inceptionv3, Xception, InceptionResNetv2, DenseNet121, and Resnet50. The models are then integrated using an ensemble technique to improve forecast accuracy. The proposed ensemble approach is more effective in classifying X-ray and CT Scan and forecasting COVID-19.

The long-term sequelae of COVID-19: an international consensus on research priorities for patients with pre-existing and new-onset airways disease.

Persistent ill health after acute COVID-19-referred to as long COVID, the post-acute COVID-19 syndrome, or the post-COVID-19 condition-has emerged as a major concern. We undertook an international consensus exercise to identify research priorities with the aim of understanding the long-term effects of acute COVID-19, with a focus on people with pre-existing airways disease and the occurrence of new-onset airways disease and associated symptoms. 202 international experts were invited to submit a minimum of three research ideas. After a two-phase internal review process, a final list of 98 research topics was scored by 48 experts. Patients with pre-existing or post-COVID-19 airways disease contributed to the exercise by weighting selected criteria. The highest-ranked research idea focused on investigation of the relationship between prognostic scores at hospital admission and morbidity at 3 months and 12 months after hospital discharge in patients with and without pre-existing airways disease. High priority was also assigned to comparisons of the prevalence and severity of post-COVID-19 fatigue, sarcopenia, anxiety, depression, and risk of future cardiovascular complications in patients with and without pre-existing airways disease. Our approach has enabled development of a set of priorities that could inform future research studies and funding decisions. This prioritisation process could also be adapted to other, non-respiratory aspects of long COVID.

Characteristics of patients in platform C19, a COVID-19 research database combining primary care electronic health record and patient reported information.

BACKGROUND: Data to better understand and manage the COVID-19 pandemic is urgently needed. However, there are gaps in information stored within even the best routinely-collected electronic health records (EHR) including test results, remote consultations for suspected COVID-19, shielding, physical activity, mental health, and undiagnosed or untested COVID-19 patients. Observational and Pragmatic Research Institute (OPRI) Singapore and Optimum Patient Care (OPC) UK established Platform C19, a research database combining EHR data and bespoke patient questionnaire. We describe the demographics, clinical characteristics, patient behavior, and impact of the COVID-19 pandemic using data within Platform C19. METHODS: EHR data from Platform C19 were extracted from 14 practices across UK participating in the OPC COVID-19 Quality Improvement program on a continuous, monthly basis. Starting 7th August 2020, consenting patients aged 18-85 years were invited in waves to fill an online questionnaire. Descriptive statistics were summarized using all data available up to 22nd January 2021. FINDINGS: From 129,978 invitees, 31,033 responded. Respondents were predominantly female (59.6%), white (93.5%), and current or ex-smokers (52.6%). Testing for COVID-19 was received by 23.8% of respondents, of which 7.9% received positive results. COVID-19 symptoms lasted ≥4 weeks in 19.5% of COVID-19 positive respondents. Up to 39% respondents reported a negative impact on questions regarding their mental health. Most (67%-76%) respondents with asthma, Chronic Obstructive Pulmonary Disease (COPD), diabetes, heart, or kidney disease reported no change in the condition of their diseases. INTERPRETATION: Platform C19 will enable research on key questions relating to COVID-19 pandemic not possible using EHR data alone.

Risk Predictors and Symptom Features of Long COVID Within a Broad Primary Care Patient Population Including Both Tested and Untested Patients.

INTRODUCTION: Symptoms may persist after the initial phases of COVID-19 infection, a phenomenon termed long COVID. Current knowledge on long COVID has been mostly derived from test-confirmed and hospitalized COVID-19 patients. Data are required on the burden and predictors of long COVID in a broader patient group, which includes both tested and untested COVID-19 patients in primary care. METHODS: This is an observational study using data from Platform C19, a quality improvement program-derived research database linking primary care electronic health record data (EHR) with patient-reported questionnaire information. Participating general practices invited consenting patients aged 18-85 to complete an online questionnaire since 7th August 2020. COVID-19 self-diagnosis, clinician-diagnosis, testing, and the presence and duration of symptoms were assessed via the questionnaire. Patients were considered present with long COVID if they reported symptoms lasting ≥4 weeks. EHR and questionnaire data up till 22nd January 2021 were extracted for analysis. Multivariable regression analyses were conducted comparing demographics, clinical characteristics, and presence of symptoms between patients with long COVID and patients with shorter symptom duration. RESULTS: Long COVID was present in 310/3151 (9.8%) patients with self-diagnosed, clinician-diagnosed, or test-confirmed COVID-19. Only 106/310 (34.2%) long COVID patients had test-confirmed COVID-19. Risk predictors of long COVID were age ≥40 years (adjusted Odds Ratio [AdjOR]=1.49 [1.05-2.17]), female sex (adjOR=1.37 [1.02-1.85]), frailty (adjOR=2.39 [1.29-4.27]), visit to A&E (adjOR=4.28 [2.31-7.78]), and hospital admission for COVID-19 symptoms (adjOR=3.22 [1.77-5.79]). Aches and pain (adjOR=1.70 [1.21-2.39]), appetite loss (adjOR=3.15 [1.78-5.92]), confusion and disorientation (adjOR=2.17 [1.57-2.99]), diarrhea (adjOR=1.4 [1.03-1.89]), and persistent dry cough (adjOR=2.77 [1.94-3.98]) were symptom features statistically more common in long COVID. CONCLUSION: This study reports the factors and symptom features predicting long COVID in a broad primary care population, including both test-confirmed and the previously missed group of COVID-19 patients.

Diagnosis of COVID-19 by exhaled breath analysis using gas chromatography-mass spectrometry.

BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has claimed over two and a half million lives worldwide so far. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is perceived to be seasonally recurrent, and a rapid noninvasive biomarker to accurately diagnose patients early on in their disease course will be necessary to meet the operational demands for COVID-19 control in the coming years. OBJECTIVE: The aim of this study was to evaluate the role of exhaled breath volatile biomarkers in identifying patients with suspected or confirmed COVID-19 infection, based on their underlying PCR status and clinical probability. METHODS: A prospective, real-world, observational study was carried out, recruiting adult patients with suspected or confirmed COVID-19 infection. Breath samples were collected using a standard breath collection bag, modified with appropriate filters to comply with local infection control recommendations, and samples were analysed using gas chromatography-mass spectrometry (TD-GC-MS). RESULTS: 81 patients were recruited between April 29 and July 10, 2020, of whom 52 out of 81 (64%) tested positive for COVID-19 by reverse transcription-polymerase chain reaction (RT-PCR). A regression analysis identified a set of seven exhaled breath features (benzaldehyde, 1-propanol, 3,6-methylundecane, camphene, beta-cubebene, iodobenzene and an unidentified compound) that separated PCR-positive patients with an area under the curve (AUC): 0.836, sensitivity: 68%, specificity: 85%. CONCLUSIONS: GC-MS-detected exhaled breath biomarkers were able to identify PCR-positive COVID-19 patients. External replication of these compounds is warranted to validate these results.

Proning reduces ventilation heterogeneity in patients with elevated BMI: implications for COVID-19 pneumonia management?

Simulations of patient-based lungs suggest that proning reduces ventilation heterogeneity in overweight and obese subjects but increases heterogeneity in non-overweight subjects. This suggests proning may be beneficial for overweight #COVID19 patients. https://bit.ly/2MfCiyk.